Overview

Dataset statistics

Number of variables13
Number of observations380905
Missing cells0
Missing cells (%)0.0%
Duplicate rows2585
Duplicate rows (%)0.7%
Total size in memory40.7 MiB
Average record size in memory112.0 B

Variable types

Numeric10
Categorical3

Alerts

quarter_end has constant value ""Constant
Dataset has 2585 (0.7%) duplicate rowsDuplicates
total_away_score is highly overall correlated with posteam_score and 2 other fieldsHigh correlation
posteam_score is highly overall correlated with total_away_score and 1 other fieldsHigh correlation
defteam_score is highly overall correlated with total_away_score and 2 other fieldsHigh correlation
score_differential is highly overall correlated with defteam_scoreHigh correlation
yardline_100 is highly overall correlated with ydsnet and 1 other fieldsHigh correlation
ydsnet is highly overall correlated with yardline_100High correlation
quarter_seconds_remaining is highly overall correlated with half_seconds_remainingHigh correlation
half_seconds_remaining is highly overall correlated with quarter_seconds_remainingHigh correlation
game_seconds_remaining is highly overall correlated with total_away_score and 2 other fieldsHigh correlation
goal_to_go is highly overall correlated with yardline_100High correlation
goal_to_go is highly imbalanced (68.4%)Imbalance
total_away_score has 92065 (24.2%) zerosZeros
posteam_score has 96109 (25.2%) zerosZeros
defteam_score has 78339 (20.6%) zerosZeros
score_differential has 70897 (18.6%) zerosZeros
ydsnet has 14361 (3.8%) zerosZeros

Reproduction

Analysis started2023-08-27 17:38:58.807121
Analysis finished2023-08-27 17:39:23.409350
Duration24.6 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

total_away_score
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.223473
Minimum0
Maximum58
Zeros92065
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:23.475538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q316
95-th percentile28
Maximum58
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4406222
Coefficient of variation (CV)0.92342614
Kurtosis0.54674777
Mean10.223473
Median Absolute Deviation (MAD)7
Skewness0.93534377
Sum3894172
Variance89.125348
MonotonicityNot monotonic
2023-08-27T11:39:23.566346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92065
24.2%
7 44070
11.6%
3 37281
9.8%
10 31256
 
8.2%
14 19681
 
5.2%
6 19384
 
5.1%
17 19214
 
5.0%
13 18657
 
4.9%
20 13122
 
3.4%
16 9195
 
2.4%
Other values (47) 76980
20.2%
ValueCountFrequency (%)
0 92065
24.2%
1 12
 
< 0.1%
2 303
 
0.1%
3 37281
9.8%
4 17
 
< 0.1%
5 336
 
0.1%
6 19384
 
5.1%
7 44070
11.6%
8 574
 
0.2%
9 7966
 
2.1%
ValueCountFrequency (%)
58 75
< 0.1%
57 1
 
< 0.1%
55 21
 
< 0.1%
54 1
 
< 0.1%
52 7
 
< 0.1%
51 57
< 0.1%
50 21
 
< 0.1%
49 107
< 0.1%
48 134
< 0.1%
47 85
< 0.1%

posteam_score
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.192862
Minimum0
Maximum61
Zeros96109
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:23.658741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q316
95-th percentile28
Maximum61
Range61
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.5105477
Coefficient of variation (CV)0.93305962
Kurtosis0.59654333
Mean10.192862
Median Absolute Deviation (MAD)7
Skewness0.94122004
Sum3882512
Variance90.450518
MonotonicityNot monotonic
2023-08-27T11:39:23.746256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96109
25.2%
7 45150
11.9%
3 34899
 
9.2%
10 31308
 
8.2%
14 21045
 
5.5%
17 19252
 
5.1%
13 18107
 
4.8%
6 16649
 
4.4%
20 13041
 
3.4%
21 9172
 
2.4%
Other values (50) 76173
20.0%
ValueCountFrequency (%)
0 96109
25.2%
1 6
 
< 0.1%
2 794
 
0.2%
3 34899
 
9.2%
4 13
 
< 0.1%
5 360
 
0.1%
6 16649
 
4.4%
7 45150
11.9%
8 531
 
0.1%
9 7013
 
1.8%
ValueCountFrequency (%)
61 6
 
< 0.1%
59 16
 
< 0.1%
58 38
< 0.1%
56 9
 
< 0.1%
55 28
 
< 0.1%
54 30
 
< 0.1%
53 6
 
< 0.1%
52 41
< 0.1%
51 88
< 0.1%
50 16
 
< 0.1%

defteam_score
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.429215
Minimum0
Maximum61
Zeros78339
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:23.845772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q317
95-th percentile31
Maximum61
Range61
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.011376
Coefficient of variation (CV)0.87594609
Kurtosis0.39619462
Mean11.429215
Median Absolute Deviation (MAD)7
Skewness0.87075602
Sum4353445
Variance100.22765
MonotonicityNot monotonic
2023-08-27T11:39:23.950309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78339
20.6%
7 45213
11.9%
3 35570
 
9.3%
10 31377
 
8.2%
14 21492
 
5.6%
17 19814
 
5.2%
13 18785
 
4.9%
6 16584
 
4.4%
20 14272
 
3.7%
24 10211
 
2.7%
Other values (50) 89248
23.4%
ValueCountFrequency (%)
0 78339
20.6%
1 7
 
< 0.1%
2 543
 
0.1%
3 35570
9.3%
4 4
 
< 0.1%
5 330
 
0.1%
6 16584
 
4.4%
7 45213
11.9%
8 522
 
0.1%
9 7229
 
1.9%
ValueCountFrequency (%)
61 16
 
< 0.1%
59 17
 
< 0.1%
58 36
 
< 0.1%
56 23
 
< 0.1%
55 48
< 0.1%
54 26
 
< 0.1%
53 14
 
< 0.1%
52 71
< 0.1%
51 113
< 0.1%
50 36
 
< 0.1%

score_differential
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2363529
Minimum-59
Maximum59
Zeros70897
Zeros (%)18.6%
Negative175308
Negative (%)46.0%
Memory size5.8 MiB
2023-08-27T11:39:24.048161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-59
5-th percentile-20
Q1-7
median0
Q34
95-th percentile17
Maximum59
Range118
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.819541
Coefficient of variation (CV)-8.7511751
Kurtosis1.3913711
Mean-1.2363529
Median Absolute Deviation (MAD)7
Skewness-0.028861485
Sum-470933
Variance117.06246
MonotonicityNot monotonic
2023-08-27T11:39:24.145852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70897
18.6%
-7 29017
 
7.6%
-3 25455
 
6.7%
7 22156
 
5.8%
3 18726
 
4.9%
-4 13819
 
3.6%
-10 13346
 
3.5%
4 12748
 
3.3%
-14 9992
 
2.6%
-6 9904
 
2.6%
Other values (101) 154845
40.7%
ValueCountFrequency (%)
-59 17
< 0.1%
-56 10
 
< 0.1%
-55 10
 
< 0.1%
-54 32
< 0.1%
-52 6
 
< 0.1%
-51 7
 
< 0.1%
-49 12
 
< 0.1%
-48 5
 
< 0.1%
-47 7
 
< 0.1%
-46 6
 
< 0.1%
ValueCountFrequency (%)
59 16
< 0.1%
56 1
 
< 0.1%
55 4
 
< 0.1%
54 6
 
< 0.1%
52 13
< 0.1%
51 4
 
< 0.1%
49 21
< 0.1%
48 11
< 0.1%
47 8
 
< 0.1%
46 6
 
< 0.1%

yardline_100
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.381137
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:24.239727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q134
median56
Q373
95-th percentile87
Maximum99
Range98
Interquartile range (IQR)39

Descriptive statistics

Standard deviation24.623895
Coefficient of variation (CV)0.47009088
Kurtosis-0.89395917
Mean52.381137
Median Absolute Deviation (MAD)19
Skewness-0.35768351
Sum19952237
Variance606.33621
MonotonicityNot monotonic
2023-08-27T11:39:24.331496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 15769
 
4.1%
75 11620
 
3.1%
60 6226
 
1.6%
70 6191
 
1.6%
65 6132
 
1.6%
69 5962
 
1.6%
71 5881
 
1.5%
55 5712
 
1.5%
66 5643
 
1.5%
74 5635
 
1.5%
Other values (89) 306134
80.4%
ValueCountFrequency (%)
1 3842
1.0%
2 2322
0.6%
3 2242
0.6%
4 2368
0.6%
5 2613
0.7%
6 2383
0.6%
7 2403
0.6%
8 2355
0.6%
9 2575
0.7%
10 2691
0.7%
ValueCountFrequency (%)
99 667
 
0.2%
98 679
 
0.2%
97 703
 
0.2%
96 861
 
0.2%
95 1129
0.3%
94 1158
0.3%
93 1194
0.3%
92 1678
0.4%
91 1810
0.5%
90 2679
0.7%

down
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
1.0
153106 
2.0
114463 
3.0
73815 
4.0
39521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1142715
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 153106
40.2%
2.0 114463
30.1%
3.0 73815
19.4%
4.0 39521
 
10.4%

Length

2023-08-27T11:39:24.414409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T11:39:24.522925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 153106
40.2%
2.0 114463
30.1%
3.0 73815
19.4%
4.0 39521
 
10.4%

Most occurring characters

ValueCountFrequency (%)
. 380905
33.3%
0 380905
33.3%
1 153106
13.4%
2 114463
 
10.0%
3 73815
 
6.5%
4 39521
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 761810
66.7%
Other Punctuation 380905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 380905
50.0%
1 153106
20.1%
2 114463
 
15.0%
3 73815
 
9.7%
4 39521
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 380905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1142715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 380905
33.3%
0 380905
33.3%
1 153106
13.4%
2 114463
 
10.0%
3 73815
 
6.5%
4 39521
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1142715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 380905
33.3%
0 380905
33.3%
1 153106
13.4%
2 114463
 
10.0%
3 73815
 
6.5%
4 39521
 
3.5%

goal_to_go
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0.0
359183 
1.0
 
21722

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1142715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 359183
94.3%
1.0 21722
 
5.7%

Length

2023-08-27T11:39:24.599994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T11:39:24.677653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 359183
94.3%
1.0 21722
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 740088
64.8%
. 380905
33.3%
1 21722
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 761810
66.7%
Other Punctuation 380905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 740088
97.1%
1 21722
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 380905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1142715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 740088
64.8%
. 380905
33.3%
1 21722
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1142715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 740088
64.8%
. 380905
33.3%
1 21722
 
1.9%

ydstogo
Real number (ℝ)

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6028826
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:24.932435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median10
Q310
95-th percentile15
Maximum50
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1032932
Coefficient of variation (CV)0.47696724
Kurtosis2.8601606
Mean8.6028826
Median Absolute Deviation (MAD)1
Skewness0.65018532
Sum3276881
Variance16.837015
MonotonicityNot monotonic
2023-08-27T11:39:25.030842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
10 174115
45.7%
1 20168
 
5.3%
5 19591
 
5.1%
6 19113
 
5.0%
7 19067
 
5.0%
8 18237
 
4.8%
4 16669
 
4.4%
9 15906
 
4.2%
3 15385
 
4.0%
2 14567
 
3.8%
Other values (37) 48087
 
12.6%
ValueCountFrequency (%)
1 20168
 
5.3%
2 14567
 
3.8%
3 15385
 
4.0%
4 16669
 
4.4%
5 19591
 
5.1%
6 19113
 
5.0%
7 19067
 
5.0%
8 18237
 
4.8%
9 15906
 
4.2%
10 174115
45.7%
ValueCountFrequency (%)
50 1
 
< 0.1%
48 2
 
< 0.1%
46 1
 
< 0.1%
44 2
 
< 0.1%
43 2
 
< 0.1%
42 2
 
< 0.1%
41 2
 
< 0.1%
40 7
< 0.1%
39 2
 
< 0.1%
38 3
< 0.1%

ydsnet
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct153
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.758171
Minimum-87
Maximum99
Zeros14361
Zeros (%)3.8%
Negative25484
Negative (%)6.7%
Memory size5.8 MiB
2023-08-27T11:39:25.122845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-87
5-th percentile-2
Q17
median22
Q345
95-th percentile75
Maximum99
Range186
Interquartile range (IQR)38

Descriptive statistics

Standard deviation24.754666
Coefficient of variation (CV)0.89179745
Kurtosis-0.6001352
Mean27.758171
Median Absolute Deviation (MAD)17
Skewness0.63075657
Sum10573226
Variance612.79348
MonotonicityNot monotonic
2023-08-27T11:39:25.216545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14361
 
3.8%
9 9988
 
2.6%
5 9486
 
2.5%
4 9047
 
2.4%
3 8718
 
2.3%
2 8532
 
2.2%
6 8278
 
2.2%
7 7951
 
2.1%
11 7691
 
2.0%
8 7503
 
2.0%
Other values (143) 289350
76.0%
ValueCountFrequency (%)
-87 1
 
< 0.1%
-84 1
 
< 0.1%
-83 1
 
< 0.1%
-77 3
< 0.1%
-73 2
< 0.1%
-69 2
< 0.1%
-68 1
 
< 0.1%
-59 2
< 0.1%
-56 1
 
< 0.1%
-55 2
< 0.1%
ValueCountFrequency (%)
99 59
 
< 0.1%
98 88
 
< 0.1%
97 88
 
< 0.1%
96 128
 
< 0.1%
95 158
 
< 0.1%
94 208
0.1%
93 154
 
< 0.1%
92 274
0.1%
91 304
0.1%
90 429
0.1%

quarter_seconds_remaining
Real number (ℝ)

HIGH CORRELATION 

Distinct901
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean430.09812
Minimum0
Maximum900
Zeros108
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:25.319980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q1183
median420
Q3666
95-th percentile863
Maximum900
Range900
Interquartile range (IQR)483

Descriptive statistics

Standard deviation271.36997
Coefficient of variation (CV)0.63094899
Kurtosis-1.2516275
Mean430.09812
Median Absolute Deviation (MAD)241
Skewness0.10432105
Sum1.6382652 × 108
Variance73641.663
MonotonicityNot monotonic
2023-08-27T11:39:25.414679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900 7734
 
2.0%
120 4317
 
1.1%
895 1135
 
0.3%
894 976
 
0.3%
896 805
 
0.2%
115 777
 
0.2%
114 699
 
0.2%
35 686
 
0.2%
859 683
 
0.2%
116 676
 
0.2%
Other values (891) 362417
95.1%
ValueCountFrequency (%)
0 108
 
< 0.1%
1 326
0.1%
2 519
0.1%
3 554
0.1%
4 536
0.1%
5 511
0.1%
6 465
0.1%
7 518
0.1%
8 535
0.1%
9 535
0.1%
ValueCountFrequency (%)
900 7734
2.0%
899 48
 
< 0.1%
898 42
 
< 0.1%
897 310
 
0.1%
896 805
 
0.2%
895 1135
 
0.3%
894 976
 
0.3%
893 651
 
0.2%
892 418
 
0.1%
891 324
 
0.1%

half_seconds_remaining
Real number (ℝ)

HIGH CORRELATION 

Distinct1801
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean837.98723
Minimum0
Maximum1800
Zeros78
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:25.508353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q1343
median826
Q31308
95-th percentile1709
Maximum1800
Range1800
Interquartile range (IQR)965

Descriptive statistics

Standard deviation542.63671
Coefficient of variation (CV)0.64754771
Kurtosis-1.2427489
Mean837.98723
Median Absolute Deviation (MAD)482
Skewness0.098046766
Sum3.1919352 × 108
Variance294454.6
MonotonicityNot monotonic
2023-08-27T11:39:25.608127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900 5306
 
1.4%
120 4103
 
1.1%
1800 2458
 
0.6%
1795 828
 
0.2%
1794 708
 
0.2%
115 599
 
0.2%
114 503
 
0.1%
1796 499
 
0.1%
35 484
 
0.1%
116 480
 
0.1%
Other values (1791) 364937
95.8%
ValueCountFrequency (%)
0 78
 
< 0.1%
1 218
0.1%
2 397
0.1%
3 417
0.1%
4 385
0.1%
5 372
0.1%
6 315
0.1%
7 347
0.1%
8 377
0.1%
9 359
0.1%
ValueCountFrequency (%)
1800 2458
0.6%
1799 41
 
< 0.1%
1798 20
 
< 0.1%
1797 148
 
< 0.1%
1796 499
 
0.1%
1795 828
 
0.2%
1794 708
 
0.2%
1793 436
 
0.1%
1792 240
 
0.1%
1791 166
 
< 0.1%

game_seconds_remaining
Real number (ℝ)

HIGH CORRELATION 

Distinct3601
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1731.2225
Minimum0
Maximum3600
Zeros36
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2023-08-27T11:39:25.711884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120
Q1819
median1796
Q32620
95-th percentile3406
Maximum3600
Range3600
Interquartile range (IQR)1801

Descriptive statistics

Standard deviation1047.4144
Coefficient of variation (CV)0.60501431
Kurtosis-1.1645925
Mean1731.2225
Median Absolute Deviation (MAD)902
Skewness0.039970015
Sum6.5943132 × 108
Variance1097077
MonotonicityNot monotonic
2023-08-27T11:39:25.812664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900 2672
 
0.7%
2700 2634
 
0.7%
1920 2081
 
0.5%
120 2022
 
0.5%
3600 1263
 
0.3%
1800 1237
 
0.3%
3595 419
 
0.1%
1795 409
 
0.1%
3594 358
 
0.1%
1794 350
 
0.1%
Other values (3591) 367460
96.5%
ValueCountFrequency (%)
0 36
 
< 0.1%
1 75
< 0.1%
2 121
< 0.1%
3 154
< 0.1%
4 128
< 0.1%
5 146
< 0.1%
6 111
< 0.1%
7 130
< 0.1%
8 128
< 0.1%
9 109
< 0.1%
ValueCountFrequency (%)
3600 1263
0.3%
3599 17
 
< 0.1%
3598 8
 
< 0.1%
3597 86
 
< 0.1%
3596 270
 
0.1%
3595 419
 
0.1%
3594 358
 
0.1%
3593 209
 
0.1%
3592 96
 
< 0.1%
3591 67
 
< 0.1%

quarter_end
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0
380905 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters380905
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 380905
100.0%

Length

2023-08-27T11:39:25.910185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T11:39:25.986801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 380905
100.0%

Most occurring characters

ValueCountFrequency (%)
0 380905
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 380905
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 380905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 380905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 380905
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 380905
100.0%

Interactions

2023-08-27T11:39:20.882717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:04.559490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:06.292520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:08.043767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:09.909966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:11.675833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:13.453796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:15.297028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:17.265996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:19.065567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:21.058998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:04.745692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:06.486550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:08.213062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:10.084485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:11.854877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:13.646825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:15.468616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:17.444649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:19.244765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:21.230201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:04.913054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:06.654784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:08.378590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:10.256749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:12.024949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:13.843917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:15.653218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:17.627841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:19.420285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:21.408900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:05.093781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:06.819759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:08.671469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:10.428180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:12.198470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:14.024638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:15.832521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:17.806668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:19.619359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:21.589016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:05.286987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:06.989534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:08.838487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:10.611378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:12.371772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:14.212249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:16.030630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:17.988838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:19.802453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:21.757043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:05.455301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:07.160814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:09.007102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:10.776125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:12.554815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:14.389940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:16.213234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:18.176959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:19.973050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:21.938380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:05.622377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:07.344839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:09.177123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:10.938596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:12.726833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:14.577093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:16.389524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:18.346211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:20.151052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:22.117984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:05.788943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:07.535931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:09.351161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:11.113618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:12.909400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:14.756433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:16.580951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:18.528179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:20.348071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:22.293107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:05.949965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:07.706536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:09.557191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:11.279155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:13.088694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:14.933971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:16.759346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:18.710881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:20.529092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:22.496461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:06.115992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:07.875552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:09.734283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:11.455727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:13.266778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:15.120399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:17.083628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:18.890179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-08-27T11:39:20.709612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-08-27T11:39:26.057568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
total_away_scoreposteam_scoredefteam_scorescore_differentialyardline_100ydstogoydsnetquarter_seconds_remaininghalf_seconds_remaininggame_seconds_remainingdowngoal_to_go
total_away_score1.0000.7200.716-0.040-0.0280.0130.009-0.187-0.347-0.7070.0050.024
posteam_score0.7201.0000.4440.452-0.0300.010-0.028-0.179-0.344-0.7180.0070.011
defteam_score0.7160.4441.000-0.5430.0060.0340.015-0.195-0.355-0.7090.0140.010
score_differential-0.0400.452-0.5431.000-0.030-0.024-0.0570.0340.0400.0400.0110.010
yardline_100-0.028-0.0300.006-0.0301.0000.215-0.6530.1000.1090.0690.0510.845
ydstogo0.0130.0100.034-0.0240.2151.000-0.192-0.005-0.011-0.0160.2920.196
ydsnet0.009-0.0280.015-0.057-0.653-0.1921.000-0.067-0.069-0.0260.0600.358
quarter_seconds_remaining-0.187-0.179-0.1950.0340.100-0.005-0.0671.0000.5770.2700.0220.047
half_seconds_remaining-0.347-0.344-0.3550.0400.109-0.011-0.0690.5771.0000.4910.0250.061
game_seconds_remaining-0.707-0.718-0.7090.0400.069-0.016-0.0260.2700.4911.0000.0190.046
down0.0050.0070.0140.0110.0510.2920.0600.0220.0250.0191.0000.013
goal_to_go0.0240.0110.0100.0100.8450.1960.3580.0470.0610.0460.0131.000

Missing values

2023-08-27T11:39:22.658766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-27T11:39:22.972305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

total_away_scoreposteam_scoredefteam_scorescore_differentialyardline_100downgoal_to_goydstogoydsnetquarter_seconds_remaininghalf_seconds_remaininggame_seconds_remainingquarter_end
100.00.00.058.01.00.0105893.01793.03593.00
200.00.00.053.02.00.052856.01756.03556.00
300.00.00.056.03.00.082815.01715.03515.00
400.00.00.056.04.00.082807.01707.03507.00
500.00.00.098.01.00.0100796.01696.03496.00
600.00.00.098.02.00.0104760.01660.03460.00
700.00.00.094.03.00.062731.01631.03431.00
800.00.00.096.04.00.082694.01594.03394.00
900.00.00.043.01.00.0103684.01584.03384.00
1000.00.00.040.02.00.0713648.01548.03348.00
total_away_scoreposteam_scoredefteam_scorescore_differentialyardline_100downgoal_to_goydstogoydsnetquarter_seconds_remaininghalf_seconds_remaininggame_seconds_remainingquarter_end
4493591212.07.05.08.02.00.0764115.0115.0115.00
4493611212.07.05.05.03.00.0464111.0111.0111.00
449362127.012.0-5.080.01.00.01019104.0104.0104.00
449363127.012.0-5.071.02.00.011982.082.082.00
449364127.012.0-5.071.03.00.011977.077.077.00
449365127.012.0-5.066.01.00.0101964.064.064.00
449366127.012.0-5.066.02.00.0101963.063.063.00
449367127.012.0-5.066.03.00.0101958.058.058.00
449368127.012.0-5.061.04.00.051938.038.038.00
4493691212.07.05.039.01.00.010-135.035.035.00

Duplicate rows

Most frequently occurring

total_away_scoreposteam_scoredefteam_scorescore_differentialyardline_100downgoal_to_goydstogoydsnetquarter_seconds_remaininghalf_seconds_remaininggame_seconds_remainingquarter_end# duplicates
53000.00.00.080.01.00.0100900.01800.03600.00125
53200.00.00.080.01.00.0101900.01800.03600.0061
54700.00.00.080.01.00.0105900.01800.03600.0061
53700.00.00.080.01.00.0102900.01800.03600.0057
54000.00.00.080.01.00.0103900.01800.03600.0056
33300.00.00.075.01.00.0100900.01800.03600.0055
37900.00.00.075.01.00.01075900.01800.03600.0052
54300.00.00.080.01.00.0104900.01800.03600.0046
55400.00.00.080.01.00.0109900.01800.03600.0034
54800.00.00.080.01.00.0106900.01800.03600.0033